Make Continual Learning Stronger via C-Flat

Model generalization ability upon incrementally acquiring dynamically updating knowledge from sequentially arriving tasks is crucial to tackle the sensitivity-stability dilemma in Continual Learning (CL). Weight loss landscape sharpness minimization seeking for flat minima lying in neighborhoods with uniform low loss or smooth gradient is proven to be a strong training regime improving model generalization compared with loss minimization based optimizer like SGD. Yet only a few works have discussed this training regime for CL, proving that dedicated designed zeroth-order sharpness optimizer can improve CL performance. In this work, we propose a Continual Flatness (C-Flat) method featuring a flatter loss landscape tailored for CL. C-Flat could be easily called with only one line of code and is plug-and-play to any CL methods. A general framework of C-Flat applied to all CL categories and a thorough comparison with loss minima optimizer and flat minima based CL approaches is presented in this paper, showing that our method can boost CL performance in almost all cases. Code will be publicly available upon publication

Validation of the children international IgA nephropathy prediction tool based on data in Southwest China

BackgroundImmunoglobulin A nephropathy (IgAN) is one of the most common kidney diseases leading to renal injury. Of pediatric cases, 25%–30% progress into end-stage kidney disease (ESKD) in 20–25 years. Therefore, predicting and intervening in IgAN at an early stage is crucial. The purpose of this study was to validate the availability of an international predictive tool for childhood IgAN in a cohort of children with IgAN treated at a regional medical centre.MethodsAn external validation cohort of children with IgAN from medical centers in Southwest China was formed to validate the predictive performance of the two full models with and without race differences by comparing four measures: area under the curve (AUC), the regression coefficient of linear prediction (PI), survival analysis curves for different risk groups, and R2D.ResultsA total of 210 Chinese children, including 129 males, with an overall mean age of 9.43 ± 2.71 years, were incorporated from this regional medical center. In total, 11.43% (24/210) of patients achieved an outcome with a GFR decrease of more than 30% or reached ESKD. The AUC of the full model with race was 0.685 (95% CI: 0.570–0.800) and the AUC of the full model without race was 0.640 (95% CI: 0.517–0.764). The PI of the full model with race and without race was 0.816 (SE = 0.006, P < 0.001) and 0.751 (SE = 0.005, P < 0.001), respectively. The results of the survival curve analysis suggested the two models could not well distinguish between the low-risk and high-risk groups (P = 0.359 and P = 0.452), respectively, no matter the race difference. The evaluation of model fit for the full model with race was 66.5% and without race was 56.2%.ConclusionsThe international IgAN prediction tool has risk factors chosen based on adult data, and the validation cohort did not fully align with the derivation cohort in terms of demographic characteristics, clinical baseline levels, and pathological presentation, so the tool may not be highly applicable to children. We need to build IgAN prediction models that are more applicable to Chinese children based on their particular data

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Incentive Mechanism of Micro-grid Project Development

Due to the issue of cost and benefit, the investment demand and consumption demand of micro-grids are insufficient in the early stages, which makes all parties lack motivation to participate in the development of micro-grid projects and leads to the slow development of micro-grids. In order to promote the development of micro-grids, the corresponding incentive mechanism should be designed to motivate the development of micro-grid projects. Therefore, this paper builds a multi-stage incentive model of micro-grid project development involving government, grid corporation, energy supplier, equipment supplier, and the user in order to study the incentive problems of micro-grid project development. Through the solution and analysis of the model, this paper deduces the optimal subsidy of government and the optimal cooperation incentive of the energy supplier, and calculates the optimal pricing strategy of grid corporation and the energy supplier, and analyzes the influence of relevant factors on optimal subsidy and incentive. The study reveals that the cost and social benefit of micro-grid development have a positive impact on micro-grid subsidy, technical level and equipment quality of equipment supplier as well as the fact that government subsidies positively adjust the level of cooperation incentives and price incentives. In the end, the validity of the model is verified by numerical analysis, and the incentive strategy of each participant is analyzed. The research of this paper is of great significance to encourage project development of micro-grids and to promote the sustainable development of micro-grids

Research on a Microgrid Subsidy Strategy Based on Operational Efficiency of the Industry Chain

Government subsidy is a powerful tool to motivate the development of a new energy industry. At the early stage of microgrid development, for the sake of the cost and benefit issue, it is necessary for the government to subsidize so as to support and promote the development of microgrids. However, a big challenge in practice is how to optimize the operational efficiency of the microgrid industry chain with varying targets and methods of subsidy. In order to explore this problem, we construct a subsidy model based on the microgrid industry chain, involving government, investor, operator, equipment supplier, and user. Through calculation and solution of this model, we obtain price and return indicators of each microgrid industry chain participant when the subsidy target differs. Based on that, we contrast and compare the optimal subsidy strategy and influencing factors when operational efficiency indicators vary. Finally, we validate and analyze this model with numerical analysis and discuss the impact of development stage, technological level, and change in subsidy amount on the operational efficiency of the microgrid industry chain and on the returns of each participant. This result is of great significance to subsidy practice for microgrids and the development of microgrids

Incentive Mechanism of Micro-grid Project Development

Due to the issue of cost and benefit, the investment demand and consumption demand of micro-grids are insufficient in the early stages, which makes all parties lack motivation to participate in the development of micro-grid projects and leads to the slow development of micro-grids. In order to promote the development of micro-grids, the corresponding incentive mechanism should be designed to motivate the development of micro-grid projects. Therefore, this paper builds a multi-stage incentive model of micro-grid project development involving government, grid corporation, energy supplier, equipment supplier, and the user in order to study the incentive problems of micro-grid project development. Through the solution and analysis of the model, this paper deduces the optimal subsidy of government and the optimal cooperation incentive of the energy supplier, and calculates the optimal pricing strategy of grid corporation and the energy supplier, and analyzes the influence of relevant factors on optimal subsidy and incentive. The study reveals that the cost and social benefit of micro-grid development have a positive impact on micro-grid subsidy, technical level and equipment quality of equipment supplier as well as the fact that government subsidies positively adjust the level of cooperation incentives and price incentives. In the end, the validity of the model is verified by numerical analysis, and the incentive strategy of each participant is analyzed. The research of this paper is of great significance to encourage project development of micro-grids and to promote the sustainable development of micro-grids

Auction Mechanism of Micro-Grid Project Transfer

Micro-grid project transfer is the primary issue of micro-grid development. The efficiency and quality of the micro-grid project transfer directly affect the quality of micro-grid project construction and development, which is very important for the sustainable development of micro-grid. This paper constructs a multi-attribute auction model of micro-grid project transfer, which reflects the characteristics of micro-grid system and the interests of stakeholders, calculates the optimal bidding strategy and analyzes the influence of relevant factors on auction equilibrium by multi-stage dynamic game with complete information, and makes a numerical simulation analysis. Results indicate that the optimal strategy of auction mechanism is positively related to power quality, energy storage quality, and carbon emissions. Different from the previous lowest price winning mechanism, the auction mechanism formed in this paper emphasizes that the energy suppliers which provide the comprehensive optimization of power quality, energy storage quality, carbon emissions, and price will win the auction, when both the project owners and energy suppliers maximize their benefits under this auction mechanism. The auction mechanism is effective because it is in line with the principle of individual rationality and incentive compatibility. In addition, the number of energy suppliers participating in the auction and the cost of the previous auction are positively related to the auction equilibrium, both of which are adjusting the equilibrium results of the auction. At the same time, the utilization rate of renewable energy and the comprehensive utilization of energy also have a positive impact on the auction equilibrium. In the end, this paper puts forward a series of policy suggestions about micro-grid project auction. The research in this paper is of great significance to improve the auction quality of micro-grid projects and promote the sustainable development of micro-grid

The Complete Mitochondrial Genome of Red Costate Tiger Moth (Aloa lactinea [Cramer, 1777]), and Phylogenetic Analyses of the Subfamily Arctiinae

Background/Objectives: Aloa lactinea, class Insecta, order Lepidoptera, superfamily Noctuoidea, family Erebidae, and subfamily Arctiinae, is a polytrophic agricultural pest. However, there are still many sequences missing for Arctiinae from mitochondrial whole-genome sequences. Methods: In this study, we determined and analyzed the complete mitochondrial genome sequence of A. lactinea. Furthermore, based on the sequencing results, we used the Bayesian inference, maximum likelihood, and maximum reduction methods to analyze the phylogenies of 18 species of the Hypophora subfamily. Results: The mitochondrial genome was found to be a circular double-stranded DNA with a length of 15,380 bp and included 13 protein-coding genes (PCGS), 22 tRNA genes, 2 rRNA genes, and one control region. With the exception of tRNASer(AGC), all the tRNA genes could form conventional clover structures. There were 23 intergenic spacer regions with lengths of 1–52 bp and six gene overlaps with lengths of 1–8 bp. The control region was located between rrnS and tRNAMet genes and comprised 303 bp and an AT content of 74.25%. Conclusions: The results showed that A. lactinea is closely related to Hyphantria cunea. Our results suggest that Syntomini is phylogenetically distinct from Arctiini and may warrant separate tribal status within Arctiinae. This study is dedicated to researching the mitochondrial genome and phylogenetic relationships of A. lactinea, providing a molecular basis for its classification